A GPU is a specialized processor. It serves as a co-processor to a central processing unit (CPU) assisting to create graphics for display. A GPU normally has a highly parallel structure, efficient floating point operations and dedicated memory making it suitable for creating three dimensional graphics. GPUs have been used to do more than create graphics. In OLAP there is an increasing demand for near real-time interactive visualization of analytical data using in-memory analysis techniques. Most performance-enhancing techniques in multi-dimensional databases involve pre-aggregation. That is aggregation or sub-aggregation are stored, which enable response times to queries. Generally a GPU is used for performing OLAP computations as GPUs are suited for repetitive calculations required in OLAP. However, GPU has a limited memory and it is not possible to fit a cube in the memory of the GPU. Hence, every time an OLAP computation is to be performed the contents of the cube, or its in-memory portion, has to be transferred to the memory of the GPU and the result has to be transferred back after the computation. When this is done in a near real-time interactive visualization scenario, the time taken for data transfer to the GPU starts dominating the actual time for the computation, thus negating much of the advantage in making the calculations in the GPU. Hence it is important to reduce the data transfer time to get maximum interactivity from the visualization.